---
title: 'Overview'
description: 'Understanding OpenLIT Operator instrumentation providers and capabilities'
---

The OpenLIT Operator supports multiple instrumentation providers, each offering different capabilities and approaches to AI observability. This overview helps you choose the right provider for your use case.

## What is Instrumentation?

Instrumentation is the process of adding observability code to your applications to collect telemetry data such as:

- **Traces**: Detailed execution flows showing request paths
- **Metrics**: Quantitative measurements of performance and usage  
- **Logs**: Structured event records for debugging and monitoring
- **Costs**: Token usage and estimated API costs

The OpenLIT Operator automatically injects instrumentation code into your applications without requiring code changes.

## Supported Providers

The operator supports four instrumentation providers:

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    Complete AI observability built on OpenTelemetry for LLMs, vector databases, GPUs and AI Agents with distributed tracing and metrics
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    OpenInference is a set of conventions and plugins that is complimentary to OpenTelemetry to enable tracing of AI applications.
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    }
    iconType="duotone"
    >
    
    OpenLLMetry is a set of extensions built on top of OpenTelemetry that gives you complete observability over your LLM application.
  </Card>
  
  <Card title="Custom" href="/latest/operator/instrumentations/python/custom" icon="gear">
    Bring your own instrumentation image with custom implementations
  </Card>
</CardGroup>

## How Instrumentation Works

<Steps>
  <Step title="Webhook Interception">
    The operator intercepts pod creation requests via an admission webhook
  </Step>
  
  <Step title="Label Matching">
    Checks if the pod matches any AutoInstrumentation selector labels
  </Step>
  
  <Step title="Init Container Injection">
    Injects an init container with the selected instrumentation provider
  </Step>
  
  <Step title="Environment Setup">
    The init container installs instrumentation packages and sets up environment variables
  </Step>
  
  <Step title="Application Start">
    Your application starts with instrumentation automatically enabled
  </Step>
  
  <Step title="Telemetry Collection">
    Traces, metrics, and costs are automatically collected and sent to your OTLP endpoint
  </Step>
</Steps>

## Provider Comparison

| Feature | OpenLIT | OpenInference | OpenLLMetry | Custom |
|---------|---------|---------------|-------------|--------|
| **AI Instrumentations** | 50+ | 30+ | 30+ | Depends |
| **Cost Tracking** | ✅ Advanced | ✅ Basic | ✅ Basic | Depends |
| **Performance Metrics** | ✅ Comprehensive | ✅ Standard | ✅ Standard | Depends |
| **Vendor Lock-in** | None | None | None | None |

## Troubleshooting

### Common Issues

<AccordionGroup>
  <Accordion title="Provider image not found">
    **Symptoms:** Init container fails with image pull errors
    
    **Solutions:**
    ```bash
    # Check image exists
    docker pull ghcr.io/openlit/openlit-ai-instrumentation:latest
    
    # Verify AutoInstrumentation config
    kubectl get autoinstrumentation -o yaml
    
    # Check operator logs
    kubectl logs -n openlit deployment/openlit-operator
    ```
  </Accordion>
  
  <Accordion title="Instrumentation packages conflict">
    **Symptoms:** Application fails to start with package conflicts
    
    **Solutions:**
    ```yaml
    # Use specific versions
    spec:
      python:
        instrumentation:
          customPackages: "langchain==0.1.0,openai==1.0.0"
    ```
  </Accordion>
  
  <Accordion title="No telemetry data">
    **Symptoms:** Applications start but no traces appear
    
    **Solutions:**
    ```bash
    # Check environment variables
    kubectl exec pod-name -- env | grep OTEL
    
    # Verify OTLP endpoint
    kubectl exec pod-name -- curl http://openlit:4318/v1/traces
    
    # Check application logs
    kubectl logs pod-name | grep -i opentelemetry
    ```
  </Accordion>
</AccordionGroup>
